Abstract
We propose a 3D model feature line extraction method using templates for guidance. The 3D model is first projected into a depth map, and a set of candidate feature points are extracted. Then, a conditional random fields (CRF) model is established to match the sketch points and the candidate feature points. Using sketch strokes, the candidate feature points can then be connected to obtain the feature lines, and using a CRF-matching model, the 2D image shape similarity features and 3D model geometric features can be effectively integrated. Finally, a relational metric based on shape and topological similarity is proposed to evaluate the matching results, and an iterative matching process is applied to obtain the globally optimized model feature lines. Experimental results showed that the proposed method can extract sound 3D model feature lines which correspond to the initial sketch template.
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References
Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679–698. [doi:10.1109/TPAMI.1986.4767851]
Catalano, C.E., Mortara, M., Spagnuolo, M., Falcidieno, B., 2011. Semantics and 3D media: current issues and perspectives. Comput. Graph., 35(4):869–877. [doi:10.1016/j.cag.2011.03.040]
Cole, F., Golovinskiy, A., Limpaecher, A., Barros, H.S., Finikelstein, A., Funkhouser, T., Rusinkiewicz, S., 2008. Where do people draw lines?. ACM Trans. Graph., 27(3), Article 88, p.1–11. [doi:10.1145/1360612.1360687]
DeCarlo, D., Finkelstein, A., Rusinkiewicz, S., Santella, A., 2003. Suggestive contours for conveying shape. ACM Trans. Graph., 22(3):848–855. [doi:10.1145/882262.882 354]
Hertzmann, A., 1999. Introduction to 3D Non-photorealistic Rendering: Silhouettes and Outlines. ACM SIGGRAPH Course Notes, p.15–29.
Hertzmann, A., 2010. Non-photorealistic Rendering and the Science of Art. Proc. 8th Int. Symp. on Non-photorealistic Animation and Rendering, p.147–157. [doi:10.1145/1809939.1809957]
Interrante, V., Fuchs, H., Pizer, S., 1995. Enhancing Transparent Skin Surfaces with Ridge and Valley Lines. Proc. 6th Conf. on Visualization, p.52–59. [doi:10.1109/VISUAL.1995.480795]
Judd, T., Durand, F., Adelson, E.H., 2007. Apparent ridges for line drawing. ACM Trans. Graph., 26(3), Article 19. [doi:10.1145/1276377.1276401]
Kalogerakis, E., Nowrouzezahrai, D., Simari, P., McCrae, J., Hertzmann, A., Singh, K., 2009. Data-driven curvature for real-time line drawing of dynamic scenes. ACM Trans. Graph., 28(1), Article 11, p.1–13. [doi:10.1145/1477926.1477937]
Kalogerakis, E., Nowrouzezahrai, D., Breslav, S., Hertzmann, A., 2012. Learning hatching for pen-and-ink illustration of surfaces. ACM Trans. Graph., 31(1), Article 1, p.1–17. [doi:10.1145/2077341.2077342]
Kraevoy, V., Sheffer, A., Michiel, P., 2009. Modeling from Contour Drawings. Proc. 6th Eurographics Symp. on Sketch-Based Interfaces and Modeling, p.37–44. [doi:10.1145/1572741.1572749]
Lafferty, J., McCallum, A., Pereira, F., 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proc. 18th Int. Conf. on Machine Learning, p.282–289.
Lee, Y., Markosian, L., Lee, S., Hughes, J.F., 2007. Line drawings via abstracted shading. ACM Trans. Graph., 26(3), Article 18, p.1–6. [doi:10.1145/1276377.1276400]
Lum, E.B., Ma, K.L., 2005. Expressive line selection by example. Vis. Comput., 21(8–10):811–820. [doi:10.1007/s00371-005-0342-y]
Mao, C., Qin, S.F., Wright, D., 2009. A sketch-based approach to human body modeling. Comput. Graph., 33(4):521–541. [doi:10.1016/j.cag.2009.03.028]
Murphy, K., Weiss, Y., Jordan, M., 1999. Loopy Belief Propagation for Approximate Inference: an Empirical Study. Proc. Conf. on Uncertainty in Artificial Intelligence, p.467–475.
Olsen, L., Samavati, F.F., Sousa, M.C., Jorge, J.A., 2009. Sketch-based modeling: a survey. Comput. Graph., 33(1): 85–103. [doi:10.1016/j.cag.2008.09.013]
Ramos, F., Fox, D., Durrant, W.H., 2007. CRF-Matching: Conditional Random Fields for Feature-Based Scan Matching. Proc. Robotics: Science and Systems, p.201–208.
Saito, T., Takahashi, T., 1990. Comprehensible Rendering of 3-D Shapes. ACM SIGGRAPH Comput. Graph., 24(4): 197–206. [doi:10.1145/97880.97901]
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Project supported by the National Natural Science Foundation of China (Nos. 61272219, 61100110, and 61021062), the National High-Tech R&D Program (863) of China (No. 2007AA01Z334), the Program for New Century Excellent Talents in University (No. NCET-04-04605), and the Science and Technology Program of Jiangsu Province, China (Nos. BE2010072, BE2011058, and BY2012190)
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Zhang, Yy., Sun, Zx., Liu, K. et al. Extracting 3D model feature lines based on conditional random fields. J. Zhejiang Univ. - Sci. C 14, 551–560 (2013). https://doi.org/10.1631/jzus.CIDE1308
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DOI: https://doi.org/10.1631/jzus.CIDE1308
Key words
- Nonphotorealistic rendering
- Model feature lines
- Conditional random fields
- Feature line metrics
- Iterative matching